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User recommendation for promoting information diffusion in social networks

Author

Listed:
  • Li, Dong
  • Wang, Wei
  • Jin, Changlong
  • Ma, Jun
  • Sun, Xin
  • Xu, Zhiming
  • Li, Sheng
  • Liu, Jiming

Abstract

Online social networks mainly hold two functions: social interaction and information diffusion. Most of existing user recommendation studies only focused on enhancing the social interaction function, but ignored the problem of how to strengthen the information diffusion function. Aiming at this drawback, this paper introduces the concept of user diffusion degree, then combines it with traditional recommendation methods for reranking recommended users. Specifically, we propose two user diffusion degree calculation methods, node granularity algorithm and community granularity algorithm, which fully exploit the community attributes of users. Experimental results on Email and Amazon datasets under Independent Cascade Model illustrate that our methods noticeably outperform traditional recommendation methods in terms of promoting information diffusion. We also find that node granularity algorithm performs better in spares networks, while community granularity algorithm is more suitable for dense networks.

Suggested Citation

  • Li, Dong & Wang, Wei & Jin, Changlong & Ma, Jun & Sun, Xin & Xu, Zhiming & Li, Sheng & Liu, Jiming, 2019. "User recommendation for promoting information diffusion in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    • Handle: RePEc:eee:phsmap:v:534:y:2019:i:c:s0378437119309008
    DOI: 10.1016/j.physa.2019.121536
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    References listed on IDEAS

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    1. David Liben‐Nowell & Jon Kleinberg, 2007. "The link‐prediction problem for social networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(7), pages 1019-1031, May.
    2. Enming Dong & Jianping Li & Zheng Xie, 2014. "Link Prediction via Convex Nonnegative Matrix Factorization on Multiscale Blocks," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-9, July.
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    Cited by:

    1. Wang, Zhiping & Yin, Haofei & Jiang, Xin, 2020. "Exploring the dynamic growth mechanism of social networks using evolutionary hypergraph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 544(C).

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